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Course Outline
AI in Credit Risk: Foundations and Potential
- Comparing traditional versus AI-driven credit risk models
- Navigating credit evaluation challenges: bias, explainability, and fairness
- Real-world case studies demonstrating AI in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative data
- Data cleaning and feature engineering for informed lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Logistic regression, decision trees, and random forests
- Gradient boosting (LightGBM, XGBoost) for enhanced scoring accuracy
- Model training, validation, and tuning methodologies
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- Underwriting and approval processes enhanced by AI
- Dynamic pricing and interest rate optimization via ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Fairness in credit models: detecting and mitigating bias
- Adherence to regulatory frameworks (e.g. ECOA, GDPR)
Generative AI in Lending Contexts
- Utilizing LLMs for application review and document analysis
- Prompt engineering for borrower communication and insights
- Synthetic data generation for model testing
Strategy and Governance for AI in Credit
- Developing internal AI capabilities versus adopting external solutions
- Model lifecycle management and governance best practices
- Future trends: real-time credit scoring and open banking integration
Summary and Future Directions
Requirements
- A solid grasp of credit risk fundamentals
- Practical experience with data analysis or business intelligence tools
- Knowledge of Python or a strong desire to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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